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Community context for mechanisms of disease dilution: insights from linking epidemiology and plant–soil feedback theory

In many natural systems, diverse host communities can reduce disease risk, though less is known about the mechanisms driving this “dilution effect.” We relate feedback theory, which focuses on pathogen‐mediated coexistence, to mechanisms of dilution derived from epidemiological models, with the cent...

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Detalles Bibliográficos
Autores principales: Collins, Cathy D., Bever, James D., Hersh, Michelle H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7317922/
https://www.ncbi.nlm.nih.gov/pubmed/32170775
http://dx.doi.org/10.1111/nyas.14325
Descripción
Sumario:In many natural systems, diverse host communities can reduce disease risk, though less is known about the mechanisms driving this “dilution effect.” We relate feedback theory, which focuses on pathogen‐mediated coexistence, to mechanisms of dilution derived from epidemiological models, with the central goal of gaining insights into host–pathogen interactions in a community context. We first compare the origin, structure, and application of epidemiological and feedback models. We then explore the mechanisms of dilution, which are grounded in single‐pathogen, single‐host epidemiological models, from the perspective of feedback theory. We also draw on feedback theory to examine how coinfecting pathogens, and pathogens that vary along a host specialist–generalist continuum, apply to dilution theory. By identifying synergies among the feedback and epidemiological approaches, we reveal ways in which organisms occupying different trophic levels contribute to diversity–disease relationships. Additionally, using feedbacks to distinguish dilution in disease incidence from dilution in the net effect of disease on host fitness allows us to articulate conditions under which definitions of dilution may not align. After ascribing dilution mechanisms to macro‐ or microorganisms, we propose ways in which each contributes to diversity–disease and productivity–diversity relationships. Our analyses lead to predictions that can guide future research efforts.